There are two major MFRL frameworks, namely Q-learning [1]} and Actor-Critic [2]}. In Actor-Critic framework, the Actor's goal is to learn a policy \(\pi (s)\)  that maximizes agents returns predicted by the Critic. A widely-used method is proximal policy optimization (PPO) [3]}, where the Actor's neural network is trained on the following loss:
\(\mathcal {L}_\pi (A_t) = -\min (\mathcal {R}_t A_t, clip(\mathcal {R}_t, 1 - \epsilon , 1 + \epsilon ) A_t)\) 
